ENBIS-18 in Nancy

2 – 25 September 2018; Ecoles des Mines, Nancy (France) Abstract submission: 20 December 2017 – 4 June 2018

Functional Regression Control Chart for Ship CO2 Emission Monitoring

5 September 2018, 09:00 – 09:30

Abstract

Submitted by
Antonio Lepore
Authors
Fabio Centofanti (University of Naples Federico II), Antonio Lepore (University of Naples Federico II), Alessandra Menafoglio (MOX - Politecnico di Milano), Biagio Palumbo (University of Naples Federico II), Simone Vantini (MOX - Politecnico di Milano)
Abstract
In many industrial processes, the quality characteristic is apt to be modelled as a function defined on a compact domain, which is usually referred to as profile. Furthermore, because of the rapid development of data acquisition systems, it is not unusual that measurements of other functional and scalar covariates are available as well.
In this context, profile monitoring methods can be formulated in order to use the additional information from these covariates as well as that in the quality characteristic.

To this end, we propose a new functional control chart that combines information from all the measures attainable, with the aim of improving the efficacy of the monitoring.

The rationale behind the proposed control chart is to monitor the residuals of a function-on-function linear regression of the quality characteristic on the covariates.

This allows monitoring the quality characteristic adjusted for the effect of the covariates and, thus, taking into account their explained variance.

A Monte Carlo simulation study is presented to quantify the performance of the proposed control chart in identifying quality characteristic mean shift, through a comparison with other functional control charts proposed in the literature.

A real case study in the shipping industry is finally presented, with the purpose of monitoring CO₂ emissions from a Ro-Pax ship owned by the shipping company Grimaldi Group and in particular detecting the CO₂ emission reductions after that a specific energy efficiency initiative (EII) was performed.

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